Title :
Kernel invariance method for relating continuous-time with discrete-time nonlinear parametric models
Author :
Zhao, Xiao ; Marmarelis, Vasilis Z.
Author_Institution :
Dept. of Electr. & Biomed. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
A method for defining the equivalence between nonlinear parametric models in continuous-time (differential equations) and discrete-time (difference equations) is presented. The method, termed “kernel invariance method”, is a conceptual extension of the “impulse invariance method” in linear system modeling. It employs the general Volterra model form of nonlinear systems and requires that the sampled continuous-time kernels be identical to the discrete-time kernels. The actual implementation of the method may become unwieldy in the general case, but it appears to be tractable in certain cases of low-order nonlinear systems. An illustrative example of a quadratic system is presented that makes use of 1st order and 2nd-order kernel invariance
Keywords :
Volterra equations; continuous time systems; difference equations; differential equations; discrete time systems; nonlinear systems; parameter estimation; signal sampling; Volterra model; continuous-time nonlinear parametric models; difference equations; differential equations; discrete-time nonlinear parametric models; impulse invariance method; kernel invariance method; linear system modeling; low-order nonlinear systems; quadratic system; sampled continuous-time kernels; Artificial intelligence; Biomedical engineering; Difference equations; Differential equations; Kernel; Nonlinear equations; Nonlinear systems; Parametric statistics; Sampling methods; Tiles;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389972